There is a current trend for capturing photographic data (pictures) of cities, streets, businesses, etc. These pictures are typically captured in a way that also captures GPS location and orientation (e.g., facing 67 degrees east). This data can then be used by mapping services, to enhance and augment the quality of the data being returned. For example, when returning a map of 123 University Avenue, Palo Alto Calif. 94301, street level pictures of this location can also be returned, which can significantly improve the user experience and the value of the map information returned.
One problem here is that the mapping from a GPS location to a street address, and vice versa, is not always very accurate. This problem can be traced to the way map data is collected. In general, the GPS location of certain “anchor” street addresses along a particular street is known, but addresses in-between these anchors are interpolated. As such, significant discrepancies can sometimes be observed between the actual GPS location of an address and the interpolated location. As a result, the street images shown by a mapping service for a particular address could end up being shifted by as much as 100 yards or more.
What is needed, therefore, are techniques that improve the accuracy of interpolated or otherwise estimated street address locations.